I build AI agents for work and kept hitting the same issue: a user corrects the agent, the session ends, and the correction is gone. Next session, same correction. I tracked it across our users and the average preference gets re-corrected 4+ times before people just give up. Existing solutions don't really solve this. Memory layers store raw conversation logs. RAG retrieves documents. Neither extracts what the user actually wants as a structured, persistent preference. So I built pref0. It does one thing: extracts structured preferences from user corrections and compounds confidence across sessions. How it works in practice. Say you're building a customer support agent:

Session 1: User says "always escalate billing issues to a human, don't try to resolve them." pref0 extracts billing_issues: escalate_to_human, confidence 0.55.

Session 4: User flags a billing ticket the agent tried to auto-resolve. pref0 reinforces the preference. Confidence hits 0.85.

Session 7: A billing issue comes in. The agent routes it to a human without being told. No correction needed.

Now multiply that across hundreds of users. Each one teaching your agent slightly different things. pref0 maintains a structured profile per user (or team, or org) that your agent reads before every response.

The API is intentionally minimal. Two endpoints: POST /track: send conversation history after a session. pref0 extracts preferences automatically. GET /profiles/{user_id}: fetch learned preferences before the agent responds.

A few design decisions: > Explicit corrections ("don't do X") score higher than implied preferences. Stronger signal. > Preferences are hierarchical: user > team > org. New team members inherit org conventions on day one. > Confidence decays over time so stale preferences don't stick forever.

This isn't a replacement for memory. Memory stores what happened. pref0 learns what the user wants. You can run both side by side.

Works with LangChain, CrewAI, Vercel AI SDK, or raw API calls. Free tier available -> https://pref0.com/docs

Would love feedback on the approach, especially from anyone building agents with repeat users.

An AI model that can read and diagnose a brain MRI in seconds

Dev with 5 of experience switched to Rails, what should I be careful about?

AlphaFace: High Fidelity and Real-Time Face Swapper Robust to Facial Pose

Scientists discover “levitating” time crystals that you can hold in your hand

Rammstein – Deutschland (C64 Cover, Real SID, 8-bit – 2019) [video]

Tell HN: Yet Another Round of Zendesk Spam

Postgres Message Queue (PGMQ)

Show HN: Django-rclone: Database and media backups for Django, powered by rclone

NY lawmakers proposed statewide data center moratorium

OpenClaw AI chatbots are running amok – these scientists are listening in

Show HN: AI agent forgets user preferences every session. This fixes it

Introduce the Vouch/Denouncement Contribution Model

Show HN: SSHcode – Always-On Claude Code/OpenCode over Tailscale and Hetzner

Microsoft appointed a quality czar. He has no direct reports and no budget

Multi-agent coordination on Claude Code: 8 production pain points and patterns

Washington Post CEO Will Lewis Steps Down After Stormy Tenure

DevXT – Building the Future with AI That Acts

A Minimal OpenClaw Built with the OpenCode SDK

The silent death of Good Code

The Internal Negotiation You Have When Your Heart Rate Gets Uncomfortable

Show HN: Glance – Fast CSV inspection for the terminal (SIMD-accelerated)

Busy for the Next Fifty to Sixty Bud

Imperative

Show HN: I decomposed 87 tasks to find where AI agents structurally collapse

I went back to Linux and it was a mistake

Octrafic – open-source AI-assisted API testing from the CLI

US Accuses China of Secret Nuclear Testing

Peacock. A New Programming Language

A postcard arrived: 'If you're reading this I'm dead, and I really liked you'

What to know about the software selloff